This paper presents sliding mode-based finite-time synchronization algorithms for bilateral shared teleoperation systems under unsymmetrical time-varying delay and uncertainty. The synchronization algorithms are designed by using Lyapunov and sliding mode control theory. First, the model-based finite-time synchronization algorithms are designed by using sliding mode control theory with the presence of time-varying delays and uncertainty. Then, robust adaptive finite-time synchronization algorithms are designed for bilateral shared teleoperation systems with the presence of time-varying delays. Adaptive learning algorithms learn and adapt with uncertainty associated with the unmodeled dynamics and external disturbances. Lyapunov analysis shows that the tracking errors between master and slave manipulators converge to the sliding surface in finite time. It also shows that the tracking errors asymptotically converge to zero on the sliding surface. The design provides finite-time convergence of the states to reach the sliding surface as opposed to asymptotic convergence-based bilateral shared teleoperation systems. The design and analysis do not use the exact bound of the uncertainty. In contrast with asymptotic design, finite-time convergence can ensure faster and more robust tracking in the presence of uncertainty and time-varying delays. Unlike reported designs, the proposed algorithms can ensure convergence with the presence of unsymmetrical time-varying delays.

In this paper, we implement and evaluate a direct image-based visual tracking system for autonomous manipulation applications. The direct image-based visual tracking method is developed to relax complex image processing tasks from traditional image and position-based visual tracking methods. The visual signals are constructed by using multiresolution coefficients. The method compares the multiresolution image transformation of the current and desired images and uses the mismatch between them to drive the system. The method needs to design a multiresolution interaction matrix with half and details images. The matrix connects the multiresolution image transformation coefficients with the velocity of the manipulator and controller. To illustrate the effectiveness, the direct image-based tracking system design is implemented and evaluated to locate the position of the valve stem and set of wrenches for autonomous grasping and manipulation for fire fighting applications.

This paper investigates distributed asymptotic consensus protocol for a group of cloud connected Euler-Lagrange nonlinear systems with the presence of bounded uncertainty. The consensus protocol is designed by combining linear sliding surface vectors with robust adaptive learning algorithms. The sliding surface is designed by comprising position and velocity signals of the leader and neighboring follower Lagrange systems. Adaptive learning algorithm uses to learn and compensate bounded uncertainty associated with parameters and other external disturbance uncertainty. Lyapunov and sliding mode control theory uses to design and illustrate the convergence of the closed loop system under proposed protocol. The convergence analysis has three parts. In first part, it proves that the position and velocity consensus error states are bounded provided that the parameter estimates are continuous and bounded by positive constant. The second part guarantees that the sliding mode motion occurs for each Lagrange system in finite-time. The third part ensures that the states for a group of follower Lagrange systems can achieve asymptotic consensus tracking provided that the interaction communication topology has a directed spanning tree. This analysis shows asymptotic consensus property of the position and velocity consensus error states on the sliding mode surface. The design and implementation of the proposed asymptotic consensus protocol is easier as it does not use the exact bound of the uncertainty.

In this paper, we propose consensus based distributed cooperative LFC schemes for leader-less networked multi-area power grid network systems in the presence of uncertainty. The LFC schemes design combine local states with the states of the neighboring area with directed communication topology. We propose two distributed cooperative LFC schemes. First, robust LFC schemes are designed by assuming that the bounds of the uncertainty associated with the power network dynamics are available. Then, we remove the demand of the bound on the uncertainty from LFC schemes by designing robust adaptive learning algorithm. Robust adaptive control terms uses to deal with the presence of uncertainty associated with the power networks and external fault disturbance. Lyapunov and graph theory uses to show that the proposed distributed cooperative design can reach an agreement with control areas and solve regulation synchronization problem. Analysis shows that the state of the control areas can reach an agreement and ensure both finite-time and asymptotic consensus property. Evaluation results on a four-area interconnected power grid networks are presented to show the effectiveness of the proposed consensus based distributed LFC algorithm for real-time applications.

This work investigates robust asymptotic consensus tracking problems for a group of cloud-connected leader-follower unmanned aerial vehicles with uncertainty. The protocols for attitude and position subsystems dynamics are constructed by using the states of the local and neighboring vehicles provided that they are connected by local area networks. Robust adaptive learning algorithms are also integrated with both protocols to learn and adapt to the modeling errors and external disturbance uncertainties. Lyapunov method and Graph theory use to prove that the proposed protocol allows the vehicles to reach an agreement with follower vehicles and track the states of the leader vehicle asymptotically. Convergence analysis shows that consensus protocol can force the states of the follower MAVs to track the state of the leader MAV asymptotically. The protocol designs are simple and easy to implement as they do not need the exact bound of the uncertainty that appears from external disturbances and the modeling errors. The design does not require the bound of the input of the leader vehicle. The protocol design can ensure faster and robust consensus in the presence of uncertainty as opposed to the convergence of other asymptotic consensus designs.

This paper investigates image-feature based visual tracking systems for autonomous mobile manipulation applications. First, we briefly present various visual tracking methods and their components for autonomous tracking applications. Second, we introduce the development process for the most popular image-feature based autonomous visual tracking system for grasping and mobile manipulation applications. Then, the application scenario for evaluation is provided with the detailed software and hardware components for the fire-fighting application. Finally, the evaluation results on a 6-DOF UR5 mobile robot manipulator arms are presented for autonomous grasping and manipulation for valve turning applications.

In this paper, we introduce decentralized robust adaptive load frequency control schemes for interconnected multi-area power system networks in the presence of uncertainty. The uncertainty in power networks may appear from load variations, modeling errors, fault disturbances and changes of the power system structure for bilateral contracts between distribution and transmission companies for future open and liberal electricity trading policy. First design comprises state feedback vector terms with the robust and adaptive terms to learn and compensate uncertainty associated with multi-area power system networks. Second design integrates state and sliding mode feedback vectors with the robust and adaptive terms to deal with uncertainty. The control algorithms are designed by using Lyapunov method. It is shown in our convergence analysis that the variation of the states of multi-area power system networks are bounded and their bounds asymptotically convergeto zero. Compared with existing results, the proposed method does not require the exact bounds of the uncertainty associated with the power system networks. The design is simple and easy to implement as it does not require the exact model and matched uncertainty of the power system networks. Evaluation results on multi-area power system networks are given to demonstrate the effectiveness of the proposed method for real-time applications.

This paper develops robust adaptive tracking synchronization protocol for a group of cloud connected leader-follower multirotor aerial vehicles (MAVs) with uncertainty. The design combines adaptive learning mechanism with sliding mode control vectors to solve consensus tracking synchronization problem for both attitude and position dynamics. The protocols are constructed by using local and neighboring states of the vehicles provided that the vehicles can share states information with neighboring vehicles via local area network. Adaptive learning algorithms are used locally for each vehicle to deal with uncertainty associated with nonlinear dynamics and uncertain flying environment. Lyapunov and sliding mode control method is employed to design and analyze asymptotic convergence of the consensus tracking error functions. The convergence analysis shows that the states of the follower vehicles can reach an agreement and synchronize to the leader vehicle achieving ensuring tracking synchronization property. The protocols design and implementation is simple as they do not require exact knowledge of the dynamical model and uncertainty.

This paper introduces consensus based leader-follower robust cooperative load frequency tracking control(LFTC) protocols for multi-area smart power grid networks. The LFC protocols combines local states with the states of the neighboring areas with directed communication topology. We first develop Robust LFTC protocols by assuming that the bounds of the uncertainty associated with power network dynamics and disturbance are known a priori. Lyapunov and graph theory used to show the finite-time convergence of the states of the follower control area power grid networks to the states of the leader control area. Then, we relax the demand of the bound on the uncertainty from LFTC protocols by integrating a robust adaptive learning algorithms. Robust adaptive learning control uses to deal with the presence of uncertainty associated with the power grid networks and disturbance. Convergence analysis of the closed-loop multi-area power grid networks are shown by using Lyapunov and graph theory. Analysis shows that the states of the follower control areas can reach an agreement and track the states of the leader control area asymptotically. Evaluation results on a four-area interconnected power grid networks are presented to show the effectiveness of the proposed consensus based distributed robust LFTC protocols for real-time applications.

This work develops delay dependent load frequency control scheme for multi-area smart power grid over open communication networks with the presence of uncertainty and unsymmetrical time varying delays. First, the design employs direct method using differential inequalities and matrix measures to derive stability conditions for power grid network systems. The design assumed that the uncertainty associated with modeling errors and external fault disturbances are bounded. The stability conditions are given together with the upper bound of the delays and the convergence rate of the solution trajectory of the closed loop system. Second, we introduce Lyapunov based indirect method to establish stability criterion for LFC systems. The stability condition is established for both symmetrical and unsymmetrical time varying delays in measurement and control channel. The design analyzes the upper bound of the delay and solution trajectory in the presence of uncertainty varying with the state and constant. Compared with the existing designs, the proposed design and analysis uses time varying delays both in measurement channel from RTU to control center and control channel from control center to power generation unit. Unlike the existing LFC schemes, the design employs the uncertainty appearing into multi-area power system networks from the modeling errors, variation of loads and other external disturbances.

In this paper, distributed robust adaptive finite-time tracking protocols are developed by using consensus mechanism for a group of second-order leader-follower nonlinear autonomous systems with the presence of uncertainty. The consensus protocols are designed by using Lyapunov and nonlinear terminal sliding mode theory. The sliding mode surface is designed by using the states of the local and neighboring systems that are shared via local area communication networks. Robust and adaptive learning algorithms are used with the protocol to learn and compensate uncertainty associated with leader and follower systems dynamics. Adaptive learning algorithms are used to adapt with the input of the leader system. Lyapunov and matrix theory together with terminal sliding mode control strategy uses to show the convergence of the finite-time consensus property. Analysis shows that robust consensus protocol allows the systems to share their states information and reach an agreement to track the states of the leader system in finite-time. The protocols design and analysis do not require the bound of the uncertainty associated with the followers and leader systems dynamics. The protocol does not use the exact bound of the input of the leader system. Finally, evaluation results are presented to demonstrate the validity of the proposed design for real-time applications.

This paper develops a new wavelet-based visual tracking system for miniature aerial vehicle (MAV) system. The visual signals for visual tracking system is formulated based on using wavelet coefficients. The design uses multiresolution interaction matrix with half and details image to relate the time-variation of wavelet coefficients with the velocity of the MAV and controller. The design is evaluated on a quadrotor MAV system to demonstrate the effectiveness of the wavelet based 6-DOF visual tracking system without using image processing unit. The evaluation results show that the MWT based linear controller can provide accuracy and efficiency without using image processing unit provided that the system does not contain any uncertainty.

This paper presents a method for maintaining high accuracy of laser-guided robotic manipulation by continuously compensating for position and alignment errors for aerospace manufacturing processes. The objectives of the method are to use the robot to perform reconfiguration in a flexible fixture and minimize the calibration work (tool, workpiece), continuously track the position and alignment of the mounted tool on the robotic arm, and minimize the positioning and alignment errors between the tool and the workpiece. The proposed method basically estimates first the transformation matrix between the 6 Degrees-of-Freedom (DoF) reflector sensor, which is mounted on the robotic arm with respect to robotic flange, and second the transformation matrix between the laser tracker frame with respect to the robot base frame. For estimating the two matrices, two solvers based on an iterative absolute orientation method and hybrid optimization solver are presented and thoroughly discussed. The calibration method is first validated in a robotic simulation environment to visualize the transformations before (initial guess) and after (final solution) calibration. The results show that the two calibration solvers are able to detect the exact poses (ground truth) from a simulated data set. Second, the method is applied to a laser assisted robotic system which is composed of Leica Absolute Tracker AT960 and the reflector Leica Tracker-Machine Control (T-Mac) as well as a 6 DoF KUKA KR 60 robotic arm. The method is used to successfully register the laser measurements with respect to the robot base frame.

In this paper, we investigate consensus based distributed robust adaptive tracking control strategy for second-order nonlinear multi-agent systems. The design only uses the states of the neighboring agents with directed communication topology in the presence of uncertainty. Robust and adaptive control algorithms are used for each agent to deal with uncertainty associated with leader input and follower agent dynamics. Lyapunov, Graph and sliding mode control theory uses to show that the proposed distributed cooperative design can reach an agreement with follower agents and track the states of the leader asymptotically. Analysis shows that robust consensus algorithm can force the states of the followers to track the sliding surface and remain there. In contrast with existing distributed design, the proposed consensus controller does not require the bound of the dynamical uncertainty and leader input for the follower agents. Evaluation results are presented to show the effectiveness of the proposed design for real-time application.

In this paper, a multiple circular contours extraction method is applied to estimate the geometric primitives of multiple circles in the three-dimensional space for robot-assisted industrial automation for manufacturing processes. The primary objective is to establish an accurate reference frame which is a major requirement in the robot-assisted riveting process for floating aircraft fasteners used in aerospace structures assembly. The reference frame that constitutes the positions, the diameters, and orientations of the fastening holes is used to minimize the positioning and alignment errors between the floating fasteners and the workpiece. The method can be divided into the following steps. Firstly, a Maximum Likelihood Estimation Sample Consensus (MLESAC) method is used to fit the plane on the point cloud and to classify the data into inliers (coplanar points) and outliers (noisy points due to strong reflections of laser on shiny metallic surfaces). Secondly, after downsampling the inliers, the data are rotated using the Rodrigues formula such that the normal direction of the estimated plane and the z-axis direction are parallel. Thirdly, the Delaunay triangulation is constructed on the rotated inliers and a confidence interval is estimated to classify the points that are located at the circular boundaries of the holes from the inliers. Fourthly, a hierarchical clustering approach is applied to partition the classified point cloud into three data sets belonging to one major hole and two minor holes. Finally, the convex hull is constructed on the clustered data sets and three circular profiles are fitted. The method is applied on a noisy experimental data and the repeatability of the outputs is discussed thoroughly. In our evaluation, the point cloud is acquired by a laser stripe sensor placed on a liner rail, which is attached on an end effector of a 6 Degrees-of-Freedom (DoF) KUKA robotic arm. The method is used to successfully automate the riveting of the fastener components on an aerospace structure.